A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments

A novel ensemble Wasserstein GAN framework for effective anomaly detection in industrial internet of things environments

Abstract Imbalanced datasets in Industrial Internet of Things (IIoT) environments pose a serious challenge for reliable pattern classification. Critical instances of minority classes (such as anomalies or system faults) are often vastly outnumbered by routine data, making them difficult to detect. T...

Full description

Saved in:
Bibliographic Details
Main Authors: Rubina Riaz, Guangjie Han, Kamran Shaukat, Naimat Ullah Khan, Hongbo Zhu, Lei Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Industrial internet of things
Data augmentation
Deep learning
SMOTE
Machine learning for IIoT
Synthetic data generation
Online Access:https://doi.org/10.1038/s41598-025-07533-1
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items